
Guilherme developed core features for the pgcomp-dashboard repository, focusing on scalable academic data extraction and robust admin tooling. He engineered a browser-based scraping framework using PHP and Symfony Panther, enabling automated collection of Google Scholar data with proxy support and resilient error handling. On the frontend, he enhanced mobile usability by implementing responsive chart rendering in React and Chart.js, adapting visualizations for different devices. Guilherme also improved admin workflows by refining URL routing and standardizing API integration with TypeScript, ensuring accurate user-scoped data retrieval. His work demonstrated depth in backend automation, frontend adaptability, and reliable cross-environment deployment practices.

January 2025 — pgcomp-dashboard/pgcomp-dashboard: Focused on admin UI reliability and user-scoped data retrieval. Delivered Admin Frontend URL Routing and UI cleanup to support /admin/ routes, standardized base API URLs for development and production environments, and simplified the admin UI by removing an XML upload placeholder. Addressed data accuracy with a User-specific Productions Data Retrieval Fix, ensuring route matching includes userId and correcting the API endpoint for fetching productions by user and type. These changes reduced edge cases, improved data accuracy for admin reports, and enhanced deployment stability.
January 2025 — pgcomp-dashboard/pgcomp-dashboard: Focused on admin UI reliability and user-scoped data retrieval. Delivered Admin Frontend URL Routing and UI cleanup to support /admin/ routes, standardized base API URLs for development and production environments, and simplified the admin UI by removing an XML upload placeholder. Addressed data accuracy with a User-specific Productions Data Retrieval Fix, ensuring route matching includes userId and correcting the API endpoint for fetching productions by user and type. These changes reduced edge cases, improved data accuracy for admin reports, and enhanced deployment stability.
December 2024 performance summary for pgcomp-dashboard/pgcomp-dashboard. Delivered mobile-responsive chart rendering with adaptive behavior and a refactor of base URL handling to improve cross-device consistency. This work enhances mobile UX, simplifies rendering logic, and strengthens the product's reliability across screen sizes.
December 2024 performance summary for pgcomp-dashboard/pgcomp-dashboard. Delivered mobile-responsive chart rendering with adaptive behavior and a refactor of base URL handling to improve cross-device consistency. This work enhances mobile UX, simplifies rendering logic, and strengthens the product's reliability across screen sizes.
Concise monthly summary for 2024-11 focusing on pgcomp-dashboard/pgcomp-dashboard: - Delivered a scalable scraping foundation for academic data extraction, enabling browser-based scraping with Symfony Panther and an Artisan command to trigger Google Scholar scraping for professors. This establishes an end-to-end workflow for data collection and preparation. - Implemented initial article scraping flow from Google Scholar professor profiles, with URL extraction and author profile linkage groundwork to support future article data collection. - Expanded scraping capabilities to recognize multiple publication types (Journal, Source, Publisher, Conference), improving data completeness and downstream analytics. - Enhanced scraping robustness with proxy support, customizable user-agents, and improved error handling and logging to mitigate IP blocks and increase reliability. - Prepared a solid technical base for scaling data collection across more professors and publications, with clear separation of concerns between scraping, data normalization, and logging.
Concise monthly summary for 2024-11 focusing on pgcomp-dashboard/pgcomp-dashboard: - Delivered a scalable scraping foundation for academic data extraction, enabling browser-based scraping with Symfony Panther and an Artisan command to trigger Google Scholar scraping for professors. This establishes an end-to-end workflow for data collection and preparation. - Implemented initial article scraping flow from Google Scholar professor profiles, with URL extraction and author profile linkage groundwork to support future article data collection. - Expanded scraping capabilities to recognize multiple publication types (Journal, Source, Publisher, Conference), improving data completeness and downstream analytics. - Enhanced scraping robustness with proxy support, customizable user-agents, and improved error handling and logging to mitigate IP blocks and increase reliability. - Prepared a solid technical base for scaling data collection across more professors and publications, with clear separation of concerns between scraping, data normalization, and logging.
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